In the field of clinical medicine, computed tomography (CT) is an effective medical imaging modality for the diagnosis of various pathologies. Compared with X-ray images, CT images can provide more information, including multi-planar slices and three-dimensional structures for clinical diagnosis. However, CT imaging requires patients to be exposed to large doses of ionizing radiation for a long time, which may cause irreversible physical harm. In this paper, we propose an Uncertainty-aware MedNeRF (UMedNeRF) network based on generated radiation fields. The network can learn a continuous representation of CT projections from 2D X-ray images by obtaining the internal structure and depth information and using adaptive loss weights to ensure the quality of the generated images. Our model is trained on publicly available knee and chest datasets, and we show the results of CT projection rendering with a single X-ray and compare our method with other methods based on generated radiation fields.

翻译：在临床医学领域，计算机断层扫描（CT）是一种有效的病理诊断医学成像方式。与X光图像相比，CT图像能为临床诊断提供更多信息，包括多平面切片和三维结构。然而，CT成像要求患者长时间暴露于大剂量电离辐射中，这可能导致不可逆的身体损伤。本文提出一种基于生成辐射场的不确定性感知MedNeRF（UMedNeRF）网络。该网络通过获取内部结构和深度信息，并采用自适应损失权重以确保生成图像质量，从而从二维X光图像中学习CT投影的连续表示。我们的模型在公开的膝关节和胸部数据集上进行训练，展示了基于单张X光图像的CT投影体绘制结果，并将我们的方法与其它基于生成辐射场的方法进行了比较。